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. 2015 Dec 8;5:17854. doi: 10.1038/srep17854

The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications

Chu Qin 1,2,3, Lin Tao 2,3, Yik Hui Phang 2, Cheng Zhang 2,4, Shang Ying Chen 2, Peng Zhang 2, Ying Tan 1, Yu Yang Jiang 1,a, Yu Zong Chen 2,b
PMCID: PMC4672303  PMID: 26644316

Abstract

Mobile health technologies to detect physiological and simple-analyte biomarkers have been explored for the improvement and cost-reduction of healthcare services, some of which have been endorsed by the US FDA. Advancements in the investigations of non-invasive and minimally-invasive molecular biomarkers and biomarker candidates and the development of portable biomarker detection technologies have fuelled great interests in these new technologies for mhealth applications. But apart from the development of more portable biomarker detection technologies, key questions need to be answered and resolved regarding to the relevance, coverage, and performance of these technologies and the big data management issues arising from their wide spread applications. In this work, we analyzed the newly emerging portable biomarker detection technologies, the 664 non-invasive molecular biomarkers and the 592 potential minimally-invasive blood molecular biomarkers, focusing on their detection capability, affordability, relevance, and coverage. Our analysis suggests that a substantial percentage of these biomarkers together with the new technologies can be potentially used for a variety of disease conditions in mhealth applications. We further propose a new strategy for reducing the workload in the processing and analysis of the big data arising from widespread use of mhealth products, and discuss potential issues of implementing this strategy.

There have been intensifying efforts to explore mobile health (mhealth) technologies for delivering healthcare at reduced costs and for facilitating more precise and personalized medicine1,2,3 which have led to 73 apps endorsed (examples in Table 1, a complete list in Supplementary Table S1) and additional ones reviewed1 by the US Food and Drug Administration (FDA) for self-diagnosing acute diseases and monitoring chronic conditions1 based on such physiological biomarkers as body temperature and brainwave4,5, and such simple-analyte biomarkers as glucose and urine protein contents4,5.

Table 1. Examples of FDA endorsed mobile apps. (For a complete list, please refer to Supplementary Table S1).

Device Name Applicant 510(k) Number Type Measure Disease
Airstrip Ob Airstrip Technologies, Lp K090269 Monitoring Fetal Heart Tracings; Maternal Contraction Pattern Obstetrics/Gynecology
Alivecor Heart Monitor For Iphone Alivecor, Inc. K122356 Monitoring Ecg Cardiovascular
Beam Brush/Beam App Beam Technologies, Llc K121165 Monitoring Brushing Usage Data Tooth Decay
Bodyguardian System Bodyguardian Control Unit Bodyguardian Connect Preventice, Inc. K121197 Monitoring Ecg; Activity; Heart Rate; Respiration Rate Cardiovascular
Cg-6108 Arrhythmia Ecg Event Recorder Card Guard Scientific Survival, Ltd. K060911 Monitoring Ecg Cardiac Arrhythmia
Customized Sound Therapy (Cst) Tinnitus Otosound Products, Llc K070599 Treatment   Tinnitus
Freestyle Tracker Diabetes Management System Abbott Diabetes Care Inc. K020866 Monitoring Glucose Diabetes
Fully Automatic Wireless Blood Pressure Wrist Monitor Andon Health Co., Ltd K121470 Monitoring Blood Pressure Cardiovascular
Iglucose System Positiveid Corporation K111932 Monitoring Glucose Diabetes
Intuition Terarecon, Inc. K121916 Data Viewer Ebt, Ct, Pet Or Mri Image  
Kd-936 Fully Automatic Wireless Blood Pressure Monitor Andon Health Co.,Ltd K120672 Monitoring Blood Pressure Cardivascular
Medicalgorithmics Real-Time Ecg Monitor And Arrhythmia Detector, Model Pocketecg Medicalgorithmics Sp Z.O.O. K090037 Monitoring Heart Beat, Rhythm Abnormalities Cardivascular
Mobile Mim Mim Software Inc. K112930 Data Viewer Spect, Pet, Ct, Mri, X-Ray And Ultrasound  
Myglucohealth Glucose Monitoring Systems Entra Health Systems, Ltd. K081703 Monitoring Glucose Diabetes
Myvisiontrack(Tm) Vital Art And Science Incorporated K121738 Monitoring Central 3 Degrees Metamorphopsia (Visual Distortion) Maculopathy
Proteus Ingestion Confinmation Systems Proteus Biomedical, Inc. K113070 Monitoring Physiological And Behavioral Metrics Including Heart Rate, Activity, Body Angle And Time-Stamped User-Logged Events General
Rhythmstat Xl Data Critical Corp. K971650 Diagnostic Ecg Cardiovascular
Sd360 Digital Recorder/Sd360 Holter Digital Recorder Northeast Monitoring, Inc. K041901 Monitoring Heart Beat Cardiovascular
Silhouette, Model 1000.01 Aranz Medical Limited K070426 Monitoring External Wounds External Wounds
Smartheart Shl Telemedicine International Ltd. K113514 Monitoring Lead Egg And Rhythm Strip Cardiovascular
Veo Multigas Monitor For Pocket Pc, Model 400221 Weissburg Associates K051857 Monitoring Carbon Dioxide; Oxygen Anesthesiology
Vestibular Analysis Apparatus Capacity Sports, Llc K121590 Monitoring Balance  
Welldoc Diabetes Manager System And Diabetes Manager Rx System Welldoc, Inc K120314 Monitoring Glucose Diabetes
Withings Blood Pressure Monitor Withings K110872 Monitoring Blood Pressure Cardiovascular
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Although these physiological and simple-analyte biomarkers cover many disease conditions, their coverage is substantially limited for such prevalent diseases as cancers, infectious, respiratory, digestive, endocrine and nervous system diseases, as indicated by the disease-coverage profiles of the 73 FDA endorsed, and 94 physiological and simple-analyte biomarker candidates described in the literatures (Fig. 1, Table 1 and 2, Supplementary Table S1 and S2). Apart from the development of more portable biomarker detection technologies, additional biomarkers are needed for fulfilling the tasks of mhealth technologies as efficient and effective means for providing wider coverage of healthcare and personalized treatments at reduced costs1,2,3.

Figure 1. Disease-coverage profiles of the biomarkers.

Figure 1

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664 (27 in clinical trial or use) non-invasive molecular biomarkers are colored in light (deep) red. 592 (69 in clinical trial or use) non-invasive molecular biomarkers are colored in light (deep) green. The 94 (13 in clinical trial or use and 73 FDA endorsed apps) physiological and conventional biomarkers are colored in light (deep) blue. Each leaf in the tree represents a specific ICD code as follows: A00-B99: infectious and parasitic diseases, C00-D49: Neoplasms, D50-D89: Diseases of the blood and related organ and immune disorders, E00-E89: Endocrine, nutritional and metabolic diseases, F01-F99: Mental, Behavioral and Neurodevelopmental disorders, G00-G99: nervous system disorders, H00-H59: eye and adnexa diseases, H60-H95: Diseases of the ear and mastoid process, I00-I99: circulatory system disorders, J00-J99: respiratory system disorders, K00-K95: digestive system disorders, L00-L99: skin and subcutaneous tissue disorders, M00-M99: musculoskeletal system and connective tissue disorders, N00-N99: genitourinary system disorders, O00-O9A: Pregnancy, childbirth and the puerperium, P00-P96: conditions originating in the perinatal period, Q00-Q99: Congenital malformations, deformations and chromosomal abnormalities, R00-R99: conditions not elsewhere classified, S00-T88: Injury, poisoning and certain other consequences of external causes, V00-Y99: External causes of morbidity, Z00-Z99: Factors influencing health status and contact with health services

Table 2. Examples of physiological biomarkers. (For a complete list of physiological biomarkers, please refer to Supplementary Table S2).

Biomarker Biomarker Type Detected Disease Disease ICD Code Clinical status
Amygdala volume Prognostic Parkinson’s disease G20, F02.3  
Ankle brachial index (ABI) Diagnostic Peripheral arterial disease I73 Used in clinic
Anterior temporal atrophy Diagnostic Frontotemporal lobar degeneration G31.0  
Carotid intima-media thickness (CIMT) Diagnostic Coronary disease I25.1
Early hypertension Theragnostic Pancreatic cancer C25 Clinical trial
EBC pH Diagnostic Asthma J45  
Electrocardiography (ECG) Prognostic Acute coronary syndrome I20.0
Hair morphology Prognostic; Theragnostic Mucopolysaccharidoses E76
Hippocampal volume Prognostic Parkinson’s disease G20, F02.3
Longitudinal MRI volumetric data Prognostic Alzheimer’s disease G30, F00 Used in clinic
Macrophage migration inhibitory factor (MIF) Diagnostic Bronchopulmonary dysplasia P27.1  
Mammographic density Diagnostic Breast cancer C50 Clinical trial
Mean width of frontal horns of lateral ventricles Prognostic Parkinson’s disease G20, F02.3  
Mean width of third ventricle Prognostic Parkinson’s disease G20, F02.3
Motor unit number estimation Monitoring Amyotrophic lateral sclerosis G12.2
Neurophysiological index Monitoring Amyotrophic lateral sclerosis G12.2
Sclerosis Prognostic Follicular lymphoma C82 Clinical trial
Single-fiber electromyography (SFEMG) Prognostic Myasthenia gravis G70.0  
Sputum cytology Diagnostic Lung carcinoma C33-C34
Total kidney volume (TKV) Prognostic Autosomal-Dominant Polycystic Kidney Disease Q61
Unilateral area of substantia nigra hyperechogenicity Prognostic Parkinson’s disease G20, F02.3
Urine osmolality Prognostic Autosomal-Dominant Polycystic Kidney Disease Q61
Voxel-based morphometry Diagnostic Amyotrophic lateral sclerosis G12.2
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Some genetic, proteomic and metabolomic molecular biomarkers have been clinically used and many more such molecular biomarker candidates (hitherto also tentatively named biomarkers) have been discovered for diagnosing and monitoring diseases, directing treatments and predicting patient responses6,7,8. Of immediate relevance to mhealth are the hundreds of literature-reported non-invasive and minimally-invasive diagnostic, prognostic and theragnotic molecular biomarkers from such non-invasive sources as urine, breath, saliva, tear, feces, sputum and oral mucosa samples (Examples in Table 3 and complete list in Supplementary Table S3) and from such minimally-invasive sources as finger-prick (the list of serum biomarkers potentially detectable from finger-prick is in Supplementary Table S4), which significantly expand the disease coverage as indicated by the disease-coverage profiles of the 664 (27 clinical trial) non-invasive and 592 serum (69 clinical trial or use) molecular biomarkers with respect to those of 73 FDA endorsed apps and 94 physiological and simple-analyte biomarkers (Fig. 1). Many biomarkers are detectable by the new biomarker-detection technologies that become increasingly portable, faster, user-friendly, inexpensive and accurate9,10,11, some of which have been explored for potential mhealth applications9,12,13,14,15.

Table 3. Examples of non-invasive molecular biomarkers. For a complete list of non-invasive molecular biomarkers, please refer to Supplementary Table S3.

Biomarker Detected Disease (ICD code) Type S Detection Sen Detection Spe Biomarker Detected Disease (ICD code) Type S Detection Sen Detection Spe
17-urine-peptide biomarker panel M00-M25 Diag U ~85% ~100% MEP1A, meprin A M30.3 Diag U ~93% ~94%
2-aminoacetophenone E84 Diag Br 0.938 0.692 Methylhistamine; interleukin-6 N30.10, N30.11 Diag U 0.7 0.724
8-hydroxy-2-deoxyguanosine (8-OHdG) P27.1 Diag U 0.857 0.611 Monoclonal free immunoglobulin light chains E85.8 Diag U 0.813 0.98
ABCA5 D07.5 Diag U ~100% N/A Monocyte chemotactic protein-1 (MCP-1) Q62.0 Diag U ~85.0% ~90.0%
Basic fibroblast growth factor C56 Diag U 0.7 0.75 N-Acetyl-β-D-glucosamindase (NAG) N02.2 Prog U 0.77 N/A
Beta2-microglobulin N15.0 Diag U 0.723 0.844 Neutrophil gelatinase-associated lipocalin (NGAL) M32 Prog U ~70% ~89%
Calprotectin K50,K51 Prog F 0.9 0.83   N14.1 Prog U 0.8 0.75
DPD C90.0 Diag U 0.889 0.833 B20 Moni; Ther U 0.94 0.71
EL, endothelial lipase protein C16 Diag U 0.79 1 N17 Diag U 1 0.98
Eosinophils J45 Diag Sp 0.86 0.88 N14.1 Diag U 0.73 1
Fibrinopeptide B I82.4,I82.5 Diag U 1 0.85 NGF N30.10, N30.11 Diag U 0.75 0.655
Fibulin-3 M15-M19,M47 Diag U 0.746 0.857 Orosomucoid O11,O14 Prog U ~56.0% ~73.0%
HLA-DR T86.1 Diag U 0.8 0.98 Podocalyxin (PODXL) C64 Diag U 1 1
IL-18 N17 Prog U >90% >90% Pyruvate kinase isoenzyme M2-PK C18-C21 Diag F 73–83% 0.82
IL-8 F40-F42 Diag U ~100% N/A S100A12 K50,K51 Diag F 0.86 0.96
  N21.0-N21.9 Diag; moni U 0.9 0.68 S100B protein S06 Prog U 0.9 0.628
          S100B; lactate/creatinine ratio G93.4 Diag U 0.99 0.97  
Kininogen B55.0 Diag U 0.9   Tim-3 T86.1 Prog U 84–87% 95–96%
Lactoferrin K50,K51 Moni F 70–100% 44–100% Trypsinogen K85 Diag U 1 0.96
Leucine-rich alpha-2-glycoprotein (LRG) K35-K37 Diag U 0.95 1 Trypsinogen activation peptide (TAP) K85 Prog U 0.917 0.897
Liver-type fatty acid-binding protein(L-FABP) N03.2 Prog; Moni U 0.875 0.905 Trypsinogen-2 K85, K86.0-K86.1 Diag U 0.81 0.97
Matrix metalloproteinase 9 (MMP 9) H16.229 Diag; Moni T 0.85 0.94 Uromodulin N02.8 Diag U 1 1
  N13.7 Diag;Prog U 0.812 0.85            
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(Diag: Diagnostic, Prog: Prognotic, Mon, Monitoring, Br: Breath, F: Feces, Sa: Saliva: Sk: Skin, Sp: Sputum, T: Tears, U: Urine, Sen: Sensitivity. Spe: Specificity).

From the investigations and opinions described in the literatures listed in Supplementary Table S3, there are good reasons to speculate the readiness of some of these technologies for mhealth applications. But before the acceptance and widespread utilization of these technologies, several key questions need to be answered or resolved. Apart from the development of more portable biomarker detection technologies, an important question is whether the new portable biomarker detection technologies are sufficiently sensitive, fast, convenient and inexpensive for biomarker detection in the typical mhealth settings (low sample volume and biomarker concentrations). Another question is whether the discovered and investigative molecular biomarkers extracted from the non-invasive and minimally invasive sources are relevant to mhealth applications in terms of the detection accuracies and the coverage of disease conditions and patient populations. The third is how to resolve the different readings generated from different mhealth devices and variations in individual operations. The fourth is how to manage the heavy workload in processing and analysing the big data arising from widespread use of mhealth devices.

Here, we address some of these questions by analysing (1) biomarker detection capability of the literature-reported new technologies with specific focus on their detection sensitivity, required sample volume, test time, and costs with respect to experimentally-determined biomarker levels in patients and the detection limits, and (2) the disease coverage, patient populations, and the diagnostic, prognostic, and theragnostic sensitivity and specificity of the literature-reported non-invasive and minimally-invasive finger-prick molecular biomarkers for mhealth applications with respect to the detection limits of the new detection technologies. We also discuss the feasibility and practical issues of adopting a new strategy for reducing the heavy workload of mhealth data processing by automated electronic pre-screening of the big biomarker screening data.

Literature Search

The detailed information of 73 mhealth apps endorsed by the US FDA was obtained by manually checking the descriptions of the apps listed in FDA 510(k) medical device database16. The physiological and molecular biomarkers were obtained by the comprehensive literature search of the Pubmed database by using the combination of the keyword “biomarker” together with one of the keywords of “clinical”, “patient”, “disease”, “drug”, and specific disease names such as “cancer”, “inflammation” and “hypertension”. We also searched and evaluated biomarker review papers from reputable journals by using the combination of the keywords “biomarker” and “review”, with the cited original articles checked to collect detailed information about the discussed biomarker, such as the name, source, specific disease and function, specificity and sensitivity of the biomarker. The detailed information of these 254 evaluated review and research papers are listed in Supplementary Table S6. Additional sources such as the abstracts of the American society of clinical oncology were also systematically searched, with 658 biomarker conference abstracts in 1995–2013 extracted and evaluated by data mining and manual curation. Non-invasive biomarkers were selected if they were detected in non-invasive tissues such as urine, breath, saliva, tear, feces, sputum and oral mucosa samples. The information of disease conditions was searched from the websites of professional medical associations such as WHO17 and American Cancer Society18, and such additional sources as reputable books and review articles, using combinations of keywords such as the disease name and “prevalence” or “incidence”. These biomarkers were organized based on their international classification ICD-10 codes19 and were displayed with respect to these codes in a tree graph by using the automatic tree generator module in iTOL20.

The performance of the biomarkers in diagnosing, prognosing or theragnosing specific conditions has been statistically measured by sensitivity (the proportion of the condition-positive samples that are correctly identified as positive) and specificity (the proportion of the condition-negative samples that are correctly identified as negative)21. Wherever reported in the literature, these statistical performance measures were recorded. Apart from the collection of the biomarker detection technologies described in our searched biomarker literatures, additional literature search was conducted for searching biomarker detection technologies of potential mhealth applications by using the keyword “biomarker” in combination with one of the keywords “detection”, “detector”, “device”, “technology”, “technique” and “assay”. These detection technologies were analysed for selecting those with potential mhealth applications based on their detection performance, portability, detection time, cost and ease of use.

New technologies for detecting non-invasive molecular biomarkers and their relevance to mhealth

The new biomarker-detection technologies combined with mobile phone or the equivalent imaging devices have been explored for detecting at least 23 molecular biomarkers including 11 non-invasive ones (Table 4). These new technologies can be categorized into four groups: (1) paper-based and mobile phone enabled, (2) paper-based, (3) mobile-phone enabled, and (4) the other point of care technologies. The first group of technologies combines innovative paper-based microfluidic analytical technologies with mobile phone enabled automated image processing tools, which are most relevant to mhealth applications because of the very low cost (~$2.60+ cost plus mobile phone), increasingly enhanced detection sensitivity (0.3–60 ng/mL, 0.13–21.3 μg/mL and 0.81–2000 ng/mL for small molecule, peptide and protein biomarkers respectively), low sample volumes (0.5–25 μL), short detection time (15–60 mins), and the convenient biomarker processing (mobile phone-based) capabilities. The recently developed paper-based microfluidic analytical technologies include paper-based enzyme-linked immunosorbent assays (P-ELISA)9,22, paper lateral flow immunoassays (P- LFIAs)12,23, and paper-based Au-nanoprobes22. These are integrated with or coupled to mobile phones equipped with the colorimetric algorithms22 and the applications for immediate data processing of the detection results without referring to peripheral equipment for read-out and analysis9.

Table 4. New biomarker detection technologies.

Information about the Biomarker used for Testing the Detection Technology
 Information about the Biomarker Detection Technology
Biomarker Biomarker molecule type Biomarker Source Detected Disease Condition (Detection Type) Biomarker Levels in Patients Biomarker Levels in Normal Population Biomarker Detection Technology Product Cost Lower Limit of Detection Upper Limit of Quantification Minimum Sample Volume Detection Time Technology Readiness for Detecting Biomarker in Non-invasive Source from Patients Reference
Paper-based and mobile-phone enabled technologies
Human epididymis protein 4 (HE4) Protein Urine Ovarian cancer (D) 364.5 ng/mL - 458.8 mg/mL 0.0574 ng/mL - 727.1 ug/mL Paper-based ELISA + smartphone N/A 19.5 ng/mL 1250 ng/mL 100 μL 5 h (may be cut to 15 min) Within range 9
Mycobacterium tuberculosis nucleic acids DNA N/A Tuberculosis (D) N/A N/A Paper-based Au-nanoprobes + smartphone N/A 10 μg/mL N/A 5 μL 65 min (2h30min including PCR amplification) N/A 12
MMP9 Protein Urine Colorectal cancer (D) N/A N/A Paper lateral flow assay + smartphone/scanner $2.60 + cost of cellphone 92 ng/mL 644 ng/mL 5 μL N/A N/A 15
Thrombin Protein Urine Thrombosis (D) N/A N/A Paper lateral flow assay + smartphone/scanner $2.60 + cost of cellphone 72 ng/mL 504 ng/mL 5 μL N/A N/A 15
Neuropeptide Y Peptide Saliva Post-traumatic stress disorder (P, T) ∼1.7–5.95 pg/mL(plasma) 0.014–0.065 pg/mL (saliva), ∼0.21–2.42 pg/mL (plasma) Paper-Based ELISA + camera/smartphone/scanner/printer Low cost 127.59 ng/mL 21.265 μg/mL 3 μL <60 min Out of range 22
Hepatitis B virus plasmid DNA DNA N/A Hepatitis B (D) N/A N/A Convective polymerase chain reaction + smartphone N/A 30 copies per reaction N/A 3 μL 20 min N/A 48
VEGF Protein Inner eye aqueous humor Proliferative diabetic retinopathy, age-related macular degeneration, retinal vein occlusion (D) 740.1 ± 267.7 pg/mL, 383 ± 155.5 pg/mL, 219.4 ± 92.1 pg/mL 14.4 ± 8.5 pg/mL Paper-based ELISA + Smartphone Cost of paper-ELISA + cost of cellphone 33.7 fg/mL 10 μg/mL 2 μL 44 min Within range 24
Paper-based technologies
Chorionic gonadotropin Protein Urine Pregnancy (D) >2.5 ng/mL <0.5 ng/mL Automated paper-based sequential multistep ELISA. + inkjet printing Low cost 0.81 ng/mL 500 ng/mL 50 μL 15–25 min Within range 49
HIV-1 envelope antigen gp41 Protein Serum HIV infection (P) N/A N/A Paper-based ELISA + scanner Cost of paper-ELISA + $100 for scanner N/A N/A <20 μL <60 min N/A 25
Anti-Leishmania antibodies Protein Canine blood Leishmaniasis (D) N/A N/A Paper-based ELISA + scanner Cost of paper-ELISA + $100 for scanner 1 mg/mL N/A μL range 60 min N/A 12
Anti-NC16A autoimmune antibodies Protein Blister fluid Bullous pemphigoid (D) N/A N/A Paper-Based ELISA + desktop scanner Cost of paper-ELISA + $100 for scanner 3 ug/mL 50 μg/mL 2 μL 70 min N/A 50
Lactoferrin Protein Tear Dry eye syndrome (D) 0.13 ± 0.22 mg/mL 2.05 ± 1.12 mg/mL An inkjet-printed microfuidic paper-based analytical device + digital camera $0.0131 per testing sheet + cost of digital camera 5 ng/mL 50 ng/mL 2.5 μL 15 min Within range after dilution 13,51
Mobile-phone enabled technologies
Plasmodium falciparum histidine-rich protein 2 (PfHRP2) Protein Serum, Saliva Malaria (D) 17–1167 pg/mL (saliva) 0 A disposable microfluidic chip + smartphone with embedded circuit N/A 16 ng/mL 1024 ng/mL 0.5 μL 15 min Out of range 26,52
Bacterial DNA DNA N/A Bacterial infection (D) N/A N/A A disposable micro?uidic chip with primers + a fluorescence detector + smartphone $350-$600 760 DNA copies per μL N/A 30 μL 30 min N/A 33
Interferon-gamma Protein N/A Latent tuberculosis (D) 48.69 ± 28.78 pg/ml (blood) 12.99 ± 5.70 pg/ml (blood) An opto-acoustic immunoassay + mobile phone technologies ( surface acoustic wave transducer, CMOS camera, LED) low cost 17.15 pg/mL 17.15 ng/mL N/A 10 min Within range 27,53
Adenovirus DNA DNA N/A Viral infection N/A N/A A microfluidic capillary array + an optical signal amplifier (multi-wavelength LEDs) + smartphone $180 for capillary array + cost of LED and smartphone 0.4 ug/mL 5 μg/mL 10 μL N/A N/A 28
Cortisol Small molecule Saliva Stress, anxiety, depression (D) 20.7–37.3 ng/mL 0.4–14.1 ng/mL Chemiluminescent lateral flow Immunoassay + smartphone with custom-designed 3D printer Low cost 0.3 ng/mL 60 ng/mL 25 μL 30 min Within range 54,55
N-terminal proBNP molecule Peptide Blood Heart failure (D,P) 1076 ± 138 pg/mL 38 ± 4 pg/mL A disposable biomarker sensing element + HDR image acquisition technique + computer screen photo-assisted technique + smartphone N/A 60 pg/mL 3000 pg/mL 150 μL 12 min Within range 30,56
IL-6 Protein Serum Cancer (P) 300- 3500 pg/mL <300 pg/mL ELISA + smartphone N/A 2 pg/mL N/A N/A 2 hour 40 min Within range 57
Albumin Protein Urine Kidney disease (D) >30–300 ug/mL <30 ug/mL Fluorescent assay in disposable test tubes + smartphone $190 + cost of phone 5–10 μg/mL 200 μg/mL 25 μL 5 min Within range 26
Other lab-on-a-chip platform technologies
Apolipoprotein A1 Protein Urine Bladder cancer (D) 207.3 -3754.7 ng/mL ~ 10 ± 8 ng/mL A negative-pressure-driven microfluidic chip magnetic bead based ELISA + optical measurment device lower costs than conventional ELISA 10 ng/mL 2000 ng/ml 14.5 μL 40 min Within range 31,32
Minimally invasive finger-prick biomarker technologies
C-reactive protein Protein Blood Prostate cancer, colorectal cancer (P), >3 ug/mL (blood) <1 ug/mL (blood) A microtiterplate based ELISA + smartphone <$660 0.3 ng/mL 81 ng/mL N/A <30 min Within range after dilution 29,58
HIV-1 gp41 and HIV-2 gp36 Protein Blood HIV infection (P) N/A N/A A low-power, low-cost and compact smartphone dongle of microfluidic ELISA $34 + + cost of cellphone 10 μg/mL N/A 2 μL 15 min N/A 59,60
N-terminal proBNP molecule Peptide Blood heart failure (D,P) 1076 +_ 138 pg/mL 38 +_ 4 pg/mL A disposable biomarker sensing element + HDR image acquisition technique + computer screen photo-assisted technique + smartphone N/A 60 pg/mL 3000 pg/mL 150 uL 12 min Within range 30,56
Antibodies against HIV Protein Blood HIV (D) >0 0 A mobile microfluidic chip for immunoassay $0.1 per cassette + $0.5 light-emitting diodes+ $6 photodetector + cell phone N/A N/A 1 uL 20 min Within range 39
Antibodies against Treponema pallidum Protein Blood syphilis (D) >0 0 A mobile microfluidic chip for immunoassay $0.1 per cassette + $0.5 light-emitting diodes+ $6 photodetector + cell phone N/A N/A 1 uL 20 min Within range 39
Prostate-specific antigen (PSA) Protein Blood Prostate cancer (D) >4 ng/mL <4 ng/mL A microfluidic purification step + label-free nanosensor detection low cost 1.5 ng/mL N/A 10 uL 20min Within range 40
Carbohydrate antigen 15.3 (CA15.3) Protein Blood Breast cancer (D) >30 U/ml <30 U/ml A microfluidic purification step + label-free nanosensor detection low cost 15 U/mL N/A 10 uL 20min Within range 40
Haemoglobin Protein Blood Anaemia (D) N/A N/A               38
Aspartate aminotransferase (AST) Protein Blood Tuberculosis/HIV (T) N/A 5−40 U/L A paper-based, multiplexed microfluidic assay <$0.10 per test 84 U/L N/A 15 uL 15 min Within range 42
Alkaline phosphatase (ALP) Protein Blood Tuberculosis/HIV (T) N/A 30−120 U/L A paper-based, multiplexed microfluidic assay <$0.10 per test 53 U/L N/A 15 uL 15 min Within range 42
Aspartate aminotransferase (AST) Protein Blood Hepatitis (D) Acute : ~400 U/L, Chronic: ~ 160 U/L 5−40 U/L A micropatterned paper-based microfluidic device + cellphone low cost 44 U/L 400 U/L 15 uL 15 min Within range 35
Alkaline phosphatase (ALP) Protein Blood Liver conditions (D) N/A 30−120 U/L A micropatterned paper-based microfluidic device + cellphone low cost 15 U/L 400 U/L 15 uL 15 min Within range 35
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The second group of technologies primarily employ innovative P-ELISA in combination with a scanner, printer or digital camera based image-processing facility to achieve a detection sensitivity as high as 33.7 fg/mL24 and 18 pM/mL25 for detecting peptide and protein biomarker respectively. The imaging processing component of these technologies may be potentially replaced by mobile phone-based ones for potential mhealth applications. The third group of technologies integrates mobile phone imaging processing tools with newly developed disposable microfluidic chip26, opto-acoustic immunoassay27, microfluidic capillary array equipped with optical signal amplifier28, microtiterplate based ELISA29 and other technologies. These technologies achieve detection sensitivity up to the level of 60–300 pg/mL for protein biomarkers29,30. Although their costs are more suitable for point of care (POC) rather than mhealth applications, the innovative design may be potentially implemented into paper-based platforms for more extensive mhealth applications. A new POC technology in the fourth group, the negative-pressure-driven microfluidic chip magnetic bead based ELISA, is capable of detecting a small molecule biomarker at sensitivity level of 0.3 ng/mL31,32. If implemented into paper-based and mobile phone-enabled platforms, this technology may potentially find wider applications for detecting small molecule biomarkers in mhealth.

Overall, 12 or 52.2% of the 23 tested molecular biomarkers are detectable by these new technologies at low concentrations (0.3–810 pg/mL and 4–50 ng/mL for 8 and 4 biomarkers respectively). Although the detectable concentrations of these 23 biomarkers are roughly 10-fold higher than those of the conventional technologies24, seven of them are nonetheless within the lower detection limit of the new technologies for non-invasive detection24,27. Of the eight biomarkers with available patient data, only two biomarkers in the corresponding non-invasive source are outside the detection limit of the new technologies. Moreover, 64.3% of these biomarkers are detectable at significantly lower sample volumes (0.5–12 μL) and shorter time (10–60 min) than the volumes (100–300 μL)13,25 and durations (up to 4h)24 of the conventional technologies. The costs of these detection devices are ~$300–$600 US dollars33. The per-test costs are in the range of 0.01–190. Therefore, the new technologies are fairly sensitive, efficient, and inexpensive for detecting a substantial percentage of the tested non-invasive biomarkers, and there is high likelihood that they can be applied for detecting other non-invasive biomarkers in mhealth applications.

The non-invasive molecular biomarkers and their relevance to mhealth

Analysis of the 664 literature-reported non-invasive molecular biomarkers (examples in Table 5 and a complete list in Supplementary Table S5) showed that 546 and 183 biomarkers are for the diagnosis and prognosis of 85 and 45 disease conditions respectively, with 31 and 14 (or 36.5% and 31.1%) of the disease conditions covered by higher number (4–22) of biomarkers and 10 and 6 (or 11.8% and 13.3%) of the disease conditions by clinically-validated/evaluated biomarkers. Among these, 21 acute diseases and 11 chronic conditions affect large populations of 239,000–235 million and 10–235 million people respectively. Therefore, exploration of these biomarkers may significantly improve the efficiency of the management of these disease conditions.

Table 5. Examples of common diseases covered by non-invasive molecular biomarkers. For a complete list, please refer to Supplementary Table S5.

Disease or Disease Class Disease ICD Code Disease Prevalence Biomarker Function Type Biomarker Molecular Type (No of Biomarkers, No in clinical use or trial) Biomarker Source Feasibility of New Tech Based Biomarker Detection Highest Biomarker Detection Sensitivity Highest Biomarker Detection Specificity Disease Form (Acute/ Chronic) Biomarker Level in Patients Biomarker Level in Normal Population Technology Readiness for Detecting Biomarkers from Non-Invasive Sources from Patients
HIV infection B20 World (35.3 M),USA (1.15 M),UK (2.2 M) Prog P (6) U ELISA 94.00% 71.00% A/C N/A N/A N/A
      Ther P (6) U ELISA 94.00% 71.00% A/C N/A 0.2–146.7 ng/mL Within range
Diabetic Nephropathy E10.2, E11.2, E12.2, E13.2, E14.2 P:World (20% - 40% of diabetes) Diag P (7) U ELISA 81.40% 62.50% C 27.3 ± 3.3 ng/μmol 0–25 ng/mg Within range
      Prog P (3) U ELISA N/A N/A C N/A N/A N/A
Type 2 diabetes E11 P:World (), USA (27.85M), Europe () Diag P (11) U ELISA ~91% ~78% C 56.9 ± 19.45 μg/mL 9.7 ±2.35 μg/mL Within range
      Prog P (3) U ELISA N/A N/A C N/A N/A N/A
Chronic stress F40-F42 P:World (40 M) Diag P (1, CT) U ELISA 100.00% N/A C 70.9 ± 19.2 pg/mg 18.8 ± 32 pg/mg Out of range
Parkinson’s disease G20 P:World (10 M),USA (1 M),UK (6.7 M) Prog Sm (1) U   N/A N/A C N/A N/A N/A
Asthma J45 P:World (235 M),USA (25 M),UK (30 M) Diag Sm (4), P (1), Cell (2) Br, Sp ELISA 73.6–86.0% 88.00% C N/A N/A N/A
      Prog Sm (2), P (1) Sm+P (1, CT), Cell (1), Sm+Cell (1) Br, Sp ELISA N/A N/A C N/A N/A N/A
Acute appendicitis K35-K37 I:USA (680,000 per year) Diag P (9) U   95.00% 100.00% A 0.9–19.3 μg/mL 0.1–0.8 μg/mL Within range
Inflammatory Bowel Disease K50,K51 P:World (0.396% population),USA (1.4 M),UK (2.5–3 M) Diag P (12, CU 2), Sm (1) Br, F ELISA 80–98%, 94% 82–96%, 76% C 2.45 ± 1.15 ng/mg 0.006 ± 0.03 ng/mg N/A
      Prog P (16, CU 2) F ELISA 80–90%, 70–100% 82–83%, 44–100% C N/A 8–213 μg/mg N/A
      Ther P (2) F ELISA N/A N/A C N/A N/A N/A
Psoriasis L40 P:World (125 M),USA (7.5 M),UK (11 M) Diag P (2), miR (4), cell (1) Sk ELISA N/A N/A C N/A N/A N/A
Arthritis M00-M25 P:World (1% of population),USA (52.5 M) Diag P (17) U   ~85% ~100% C 191.7–313.4 ng/mmol 129.25 -486.85 ng/mmol Within range
      Prog P (1) U ELISA N/A N/A C N/A N/A N/A
Osteoarthritis M15-M19, M47 P:World (26.9 M) Diag P (3), Sm (1), Pep (1), Modified Pep (2, CT 1) U ELISA 74.60% 85.70% C 191.4 pM 144.4 pM Almost within range
      Prog Sm (1), Pep (3), Modified Pep (2) U   N/A N/A C N/A N/A N/A
Acute kidney injury N17 P:USA (1–7.1% of all hospital admissions) Diag P (15, CU 2, CT 3) U ELISA 69–100%, 73–100% 85–98% A 50.5–205.9 ng/mL 5.7–17.7 ng/mL Within range
      Prog P (2, CT 1) U ELISA >90% >90% A 0–955 pg/mL 0–173 pg/mL Out of range
Urolithiasis N21.0-N21.9 P:USA (7% of women and 12% of men) Diag P (3) U ELISA 90.00% 68.00% C 104.66 ± 159.70 pg/mg 7.76 ± 8.90 pg/mg Out of range
      Prog P (1) U ELISA N/A N/A C 104.66 ± 159.70 pg/mg 7.76 ± 8.90 pg/mg Out of range
Interstitial cystitis N30.10, N30.11 P:USA (8 million women) Diag P (7), Sm (2) U ELISA 70.00% 72.40% C 0.25 ± 0.1 pg/mg 0.9 ± 0.4 pg/mg Out of range
Pre-eclampsia O11,O14 P:USA (3–4% baby-delivery women) Diag P (9) U ELISA N/A N/A A 2.11 mg/mL 0.014 mg/mL Within range after dilution
      Prog P (4) U ELISA ~56% ~73% A N/A N/A N/A
Traumatic brain injury (TBI) S06 P:USA (823.7 in 100,000) Prog P (1) U ELISA 90.00% 62.80% A/C 0.025 ng/mL 0.02–1.35 ng/mL Out of range
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(Diag, Prog, Br, F, Sa, Sk, Sp, T, U are the same as in Table 3,Ther: Theragnostic, P: Protein, Sm: Small molecule, Pep: Peptide, miR: microRNA, CU: Clinical use, CT: Clinical trial, combi: combination, A: acute, C:Chronic).

The diagnostic performance of 88 (or 29.7%) of the 296 diagnostic biomarkers for 43 diseases and the prognostic performance of 24 (25.5%) of the 94 prognostic biomarkers for 14 conditions have been reported in the literature (examples in Tables 3 and 5 and a complete list in Supplementary Table S3, S5) Their performances have been typically measured by sensitivities (the rates for positive identification of disease conditions) and specificities (the rates for correct classification of the negatives). The sensitivities and specificities of the majority of these biomarkers are ≥85% and ≥80% for diagnosis, and ≥80% and ≥80% for prognosis respectively, which are roughly at the ≥90% sensitivity and ≥90% specificity levels of the good biomarkers21. Therefore, a substantial percentage of these non-invasive biomarkers are expected to be potentially useful for pre-screening patients in need of further evaluations in mhealth applications.

The utility of these biomarkers for mhealth applications also depends on whether they are detectable by the new detection technologies, i.e., whether the levels of these biomarkers in the non-invasive sources from the patients are within the detection range of the new detection technologies. We searched from the literatures the corresponding biomarker levels for 35 diseases (Supplementary Table S5, examples in Table 5) and compared them to the detection limits of the new technologies. Our analysis showed that 26 (or 74.3%) of the 35 disease conditions with searchable information, including 8 disease conditions with large patient populations, have one or more biomarker detectable by the new technologies (Table 5), suggesting that a substantial percentage of the disease conditions including those with large patient populations may be partly covered by the new technologies.

The potential of the minimally invasive finger-prick biomarker technologies for mhealth applications

The minimally invasive finger-prick biomarker technologies have been developed for POC applications11. Because of their improved detection performance34, portability35 and ease of use36, and because of their decreased detection time34, some of these technologies when combined with smartphone-based processing technologies may find potential mhealth applications. Serum biomarkers are known to be detectable at finger-prick albeit at altered concentrations and thus at re-adjusted detection cut-off values37,38. Therefore, one can hypothesize that most of the serum biomarkers of sufficient level of concentrations may be potentially detectable by finger-prick biomarker technologies. The application of these technologies in mhealth significantly expands the coverage of disease conditions because some biomarkers not found in urine are in the serum (e.g. it has been reported that the blood contains the common markers of liver function that are not found in urine35). Our own literature search results showed that the literature-reported serum biomarkers and biomarker candidates cover additional 62 disease conditions beyond those covered by the existing physiological, simple-analyte, and the non-invasive molecular biomarkers and biomarker candidates (Fig. 1 and Supplementary Table S4).

Moreover, the finger-prick biomarker technologies can potentially have more enhanced capabilities in detecting the biomarkers of low concentrations. The levels of biomarkers in blood are typically more concentrated than those biomarkers collected from the non-invasive urine, breath, saliva, tear, feces, sputum or oral mucosa sources{Song, 2014 #89} {Abdalla, 2012 #115}. For those biomarkers with concentrations in the non-invasive and finger-prick sources below and above the detection limit of the mhealth biomarker technologies respectively, some of them are potentially detectable by using finger-prick biomarker technologies even if they are undetectable by the non-invasive biomarker technologies.

Several new technologies have been developed with potential applications for detecting serum biomarkers from a drop of blood (Table 4). To enable the purification and detection of serum biomarkers, specially designed fluid handling and silver reduction devices have been combined with the ELISA microfluidic chip for simplified biomarker detection, which enables the detection of an HIV biomarker from 1 μl of unprocessed whole blood in <15 min39. In another design, a microfluidic purification chip was developed for simultaneously capturing multiple biomarkers from blood samples and releasing them into purified buffer for sensing by a silicon nanoribbon detector, which was able to detect two model cancer antigens from a 10 ml sample of whole blood in <20 min40. A micropatterned paper device that combines a filter membrane and a patterned paper chip for achieving blood plasma erythrocyte separation and biomarker detection from the blood from a fingerstick, which is capable of detecting protein biomarkers at ~50 g/L concentrations35. Progress has been made in developing plasmonic ELISA for the ultrasensitive detection of disease biomarkers with the naked eye with the ability to detect biomarkers in whole serum at the ultralow concentration of 10−18 g mL−1 41.

We have found the reports about the detection of 12 serum biomarkers by means of these new technologies (Table 4). Overall, 5 or 42% of the 12 biomarkers are detectable at concentrations of <1.5 ng/mL. Considering that many serum biomarker concentrations are higher than those collected from the urine or other non-invasive sources, the relevant technologies may be extended for the detection of a more variety of low concentration biomarkers than those coverable by the non-invasive biomarker technologies. These technologies enable serum biomarker detection mostly at low sample volumes of 1–10 uL and short time of 12–30 min comparable to those of the non-invasive biomarker technologies. The cost of a microtiterplate based ELISA device coupled with a smartphone is <$66029. The per test costs of these technologies are in the range of $0.1–34. Three studies reported the sensitivity and specificity of five serum biomarkers, which are in the range of 82–100% (vast majority >90%) and 78%-100% respectively38,39,42. Therefore, these new technologies are fairly sensitive, efficient, and inexpensive for detecting a substantial percentage of the tested serum biomarkers with potential mhealth applications.

Coping with the heavy workload in mhealth: Feasibility of automated electronic pre-screening of big mhealth data

There are concerns about the increased workload in processing and analysing the big data arising from widespread use of mhealth devices1. On the hand, mhealth devices as digital tools may conveniently facilitate electronic pre-screening of the biomarker readings for filtering potential patients likely in need of further attention and evaluation, which helps to significantly reduce the workload. A digitally-coded biomarker, disease and therapeutic information processing system may be developed for automatically receiving, processing, pre-screening, and dispatching the biomarker readings transmitted from mhealth devices (Fig. 2).

Figure 2. Flow chart of mhealth biomarker detection and automated data processing procedures.

Figure 2

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(Figure drawn by C.Q.).

It is feasible to develop such a system using available tools such as the International Classification of Diseases (ICD) codes for defining, studying and managing diseases and treatments43, the Systematized nomenclature of medicine for clinical documentation and reporting44, the Unified medical language system for biomedical terminology45, the Therapeutic target database biomarker and target information and links to the ICD and drug codes46, and the Drugbank drug information47. Further efforts are needed for additional information refinement and integration, determination and clinical validation of biomarker pre-screening thresholds, and development and education of testing protocols. There are also potential issues arising from missed detection or misidentification by an electronic system, lack of data security and insufficient regulation standards.

Concluding Remarks

Molecular biomarker-based mobile health technologies have the potential to significantly improve the efficiency and quality of healthcare for a variety disease conditions particularly those with large patient populations that cannot be solely covered by physiological and simple-analyte biomarkers. Some of these biomarkers combined with the new detection technologies are readily applicable for mhealth applications. The increased workload in processing and analyzing high volumes of mhealth data may be efficiently managed by an electronic system that facilitate automatic pre-screening and analysis of the biomarker data for filtering potential patients likely in need of further attention and evaluation.

Additional Information

How to cite this article: Qin, C. et al. The Assessment of the Readiness of Molecular Biomarker-Based Mobile Health Technologies for Healthcare Applications. Sci. Rep. 5, 17854; doi: 10.1038/srep17854 (2015).

Supplementary Material

Supplementary Table S1
srep17854-s1.doc (131.5KB, doc)
Supplementary Table S2
srep17854-s2.doc (102.5KB, doc)
Supplementary Table S3
srep17854-s3.doc (716KB, doc)
Supplementary Table S4
srep17854-s4.doc (845.5KB, doc)
Supplementary Table S5
srep17854-s5.doc (264.5KB, doc)
Supplementary Table S6
srep17854-s6.doc (858KB, doc)

Acknowledgments

This work was supported in part by the Shenzhen SZSITIC grants JSGG20141016150327538, JCYJ20140509151735023, JCYJ20140827150509058, and 20150113A0410006, and Singapore Academic Research Fund R-148-000-208-112.

Footnotes

Author Contributions Y.Z.C. and Y.Y.J. designed the study. C.Q.,Y.H.P. and Y.Z.C. undertook data collection. C.Q., L.T.,Y.H.P., C.Z., S.Y.C., P.Z., Y.T. and Y.Z.C. analyzed the data and developed drafts of the manuscript. C.Q., L.T., C.Z., S.Y.C., P.Z., Y.T., Y.Y.J. and Y.Z.C. contributed to interpretation of the results, drafting of the paper and revisions of the manuscript. All authors contributed to and approved the final draft for publication.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table S1
srep17854-s1.doc (131.5KB, doc)
Supplementary Table S2
srep17854-s2.doc (102.5KB, doc)
Supplementary Table S3
srep17854-s3.doc (716KB, doc)
Supplementary Table S4
srep17854-s4.doc (845.5KB, doc)
Supplementary Table S5
srep17854-s5.doc (264.5KB, doc)
Supplementary Table S6
srep17854-s6.doc (858KB, doc)

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